Using data from a large, six-year study, they identified 91 people whose blood sugar rose during the study period and compared them to 273 people whose glucose levels remained in the normal range.

They found the short sleepers were far more likely to develop impaired fasting glucose -- a condition that can lead to type 2 diabetes -- during the study period than those who slept six to eight hours.

One might ask any number of questions—for example, how did they measure the number of hours slept—but I want to focus on the interpretation.

The Reuters article accurately stated what was observed:

people in [the] study who slept less than six hours were 4.5 times more likely to develop abnormal blood sugar readings in six years compared with those who slept longer.

This is an example of descriptive statistics: it simply describes what happened.

In contrast, the CBC article refers not to what did happen, but to what will happen (at least as "new U.S. research suggests"). This is a jump from the sample to the population, and into the realm of inferential statistics. Presumably the authors applied valid statistical methodology in making their inferences. Unfortunately, we can't check because their results are not yet published. They were presented this past Wednesday at the American Heart Association's 49th Annual Conference in Florida. I will not comment here on whether it is appropriate for the media to report on results that have not yet been published in a peer-reviewed medical journal.

There is, however, a second inferential leap here. The CBC article suggested that inadequate sleep is "a culprit" in diabetes. Causal inferences like this are notoriously slippery. Could it not be that the development of diabetes can lead to sleep difficulties? Or perhaps there is some unidentified factor that causes both impaired glucose function and sleep difficulties? Reuters reports that the authors adjusted for age, obesity, heart rate, high blood pressure, family history of diabetes and symptoms of depression. But one can imagine any number of other possible factors. Causal inferences are extremely difficult, and at the very least they deserve cautious interpretation.

I believe that inference comes naturally to human beings; indeed this is perhaps our most distinctive evolutionary adaptation. We are programmed to infer. But like other aspects of our evolutionary inheritance, this can sometimes be maladaptive in the modern world. Consider for example the drive to consume calorie-laden goodies, which for most of human history was highly adaptive. Today, the causal chain between such behaviour, obesity, and diabetes is all too evident.